{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pyMRST_oil_slightly_comp.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNgUY15/3eQe+5dUrKCINvE",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyMRST/blob/main/notebooks/pyMRST_oil_slightly_comp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXXDnZOgYG_Q"
      },
      "source": [
        "# Simulation of Slightly Compressible Oil Reservoir with PyMRST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3_fpJXZ8d0G",
        "outputId": "cf88d7e8-7ff3-4b08-87d5-9db058492ac2"
      },
      "source": [
        "# Clone pyMRST\n",
        "!git clone https://github.com/yohanesnuwara/pyMRST"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyMRST'...\n",
            "remote: Enumerating objects: 280, done.\u001b[K\n",
            "remote: Counting objects: 100% (148/148), done.\u001b[K\n",
            "remote: Compressing objects: 100% (146/146), done.\u001b[K\n",
            "remote: Total 280 (delta 74), reused 0 (delta 0), pack-reused 132\u001b[K\n",
            "Receiving objects: 100% (280/280), 825.85 KiB | 15.01 MiB/s, done.\n",
            "Resolving deltas: 100% (120/120), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqfpKzMp8zZk"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Add directory where you install PyMRST\n",
        "import sys\n",
        "sys.path.append(\"/content/pyMRST\") \n",
        "\n",
        "import pymrst\n",
        "from pymrst_units import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OIu4smY9jqx"
      },
      "source": [
        "# Setup PyMRST (Takes about 3 minutes)\n",
        "pymrst.setup()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElR0G3Olo0zc"
      },
      "source": [
        "# Geometry\n",
        "nx, ny, nz = 30, 20, 10\n",
        "lx, ly, lz = 200, 200, 50\n",
        "\n",
        "# Reservoir property                                         \n",
        "poro = dict({\"type\": \"heterogeneous\", \n",
        "             \"field\": \"gaussian\", \n",
        "             \"min\": 0.2, \"max\": 0.4, \"std\": 2.5})\n",
        "\n",
        "k = dict({\"type\": \"heterogeneous\", \n",
        "          \"field\": \"kozeny\"})\n",
        "\n",
        "ntg = dict({\"type\": \"heterogeneous\", \n",
        "            \"field\": \"lognorm\", \n",
        "            \"min\": 0.4, \"max\": 0.6})\n",
        "\n",
        "# Rock property\n",
        "rock = dict({\"c\": 1e-6*(1/1e+5), # Rock compressibility at ref pressure, 1/bar to 1/Pa\n",
        "             \"p_r\": 200*1e+5}) # Reference pressure, bar to Pa\n",
        "\n",
        "# Fluid property: 1-phase oil\n",
        "fluid = dict({\"type\": \"oil\", \n",
        "              \"mu\": 5 * 0.001, # cp to Pa.s \n",
        "              \"rho_r\": 850, # Density at reference pressure\n",
        "              \"rhoS\": 750, # Density on the surface\n",
        "              \"p_r\": 200 * 1E+5, # Reference pressure, bar to Pa\n",
        "              \"c\": 1e-3*(1/1E+5)}) # Fluid compressibility, 1/bar to 1/Pa \n",
        "\n",
        "# Boundary \n",
        "bc_front = dict({\"type\": \"fluxside\", \"value\": 50 * 1.84e-6}) # bbl/d to m3/s\n",
        "bc_back = dict({\"type\": \"fluxside\", \"value\": 0})\n",
        "bc_left = dict({\"type\": \"fluxside\", \"value\": 100 * 1.1574e-5}) # m3/d to m3/s\n",
        "bc_right = dict({\"type\": \"pside\", \"value\": 100 * 1e+5}) # bar to Pa\n",
        "\n",
        "# Well\n",
        "# cell_loc1 = np.concatenate((np.arange(311,381+10,10), np.arange(411,481+10,10),\n",
        "#                             np.arange(412,482+10,10), np.arange(312,382+10,10), \n",
        "#                             np.array([491,391])))\n",
        "\n",
        "# well = dict({\"cell_loc\": [cell_loc1],\n",
        "#              \"type\": [\"bhp\"],\n",
        "#              \"value\": [100*1e5],\n",
        "#              \"radius\": [.1],\n",
        "#              \"skin\": [0],\n",
        "#              \"direction\": [\"y\"]})\n",
        "\n",
        "cell_loc1 = np.arange(10+60, nx*ny*nz, nx*ny)\n",
        "\n",
        "well = dict({\"cell_loc\": [cell_loc1], \n",
        "             \"type\": [\"bhp\"], \n",
        "             \"value\": [100*1e5], # bar to Pa \n",
        "             \"radius\": [.1],\n",
        "             \"skin\": [0], \n",
        "             \"direction\": [None]}) \n",
        "\n",
        "# Time step\n",
        "numSteps, totTime = 52, 365\n",
        "steps = [2, 5, 10, 20]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZb-gL8AjXH4"
      },
      "source": [
        "# Execute function\n",
        "pymrst.write_input(nx, ny, nz, lx, ly, lz, poro, k, rock, fluid, well, \n",
        "                   bc_front, bc_back, bc_left, bc_right, numSteps, totTime, steps)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwKby0wWcFoO"
      },
      "source": [
        "# def oil_1phase():\n",
        "#   \"\"\"\n",
        "#   MRST Slightly Compressible Oil Simulation (Constant Viscosity over Pressure)\n",
        "#   \"\"\"\n",
        "#   import oct2py as op\n",
        "#   # Execute simulation program \"oil_1phase.m\"\n",
        "#   # After executed, new .mat files (that contains PRESSURE, PORO, PERM result)\n",
        "#   # is created inside new directory \"result_oil_1phase\"\n",
        "#   # !octave -W /content/pyMRST/oil_1phase.m\n",
        "#   octave = op.Oct2Py()\n",
        "#   octave.run(\"/content/pyMRST/oil_1phase.m\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yA3QfY2ycoZ_",
        "outputId": "629af601-f1e5-4b91-a50e-4edd927cc772"
      },
      "source": [
        "# Execute simulator\n",
        "# Simulator results are added in new directory \"result_oil_1phase\"\n",
        "pymrst.oil_1phase()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "warning: function /content/mrst-core/utils/mex/md5sum.m shadows a core library function\n",
            "\n",
            "Time step 1: Time 0.00 -> 7.02 days\n",
            "  Iteration   1:  Res = 1.0021e+07\n",
            "  Iteration   2:  Res = 2.8867e-02\n",
            "  Iteration   3:  Res = 2.0742e-05\n",
            "  Iteration   4:  Res = 8.9843e-12\n",
            "\n",
            "Time step 2: Time 7.02 -> 14.04 days\n",
            "  Iteration   1:  Res = 1.1890e-01\n",
            "  Iteration   2:  Res = 8.0079e-04\n",
            "  Iteration   3:  Res = 4.6047e-08\n",
            "\n",
            "Time step 3: Time 14.04 -> 21.06 days\n",
            "  Iteration   1:  Res = 7.5714e-02\n",
            "  Iteration   2:  Res = 3.2365e-04\n",
            "  Iteration   3:  Res = 6.2338e-09\n",
            "\n",
            "Time step 4: Time 21.06 -> 28.08 days\n",
            "  Iteration   1:  Res = 6.4646e-02\n",
            "  Iteration   2:  Res = 2.1293e-04\n",
            "  Iteration   3:  Res = 2.2391e-09\n",
            "\n",
            "Time step 5: Time 28.08 -> 35.10 days\n",
            "  Iteration   1:  Res = 5.8855e-02\n",
            "  Iteration   2:  Res = 1.7235e-04\n",
            "  Iteration   3:  Res = 1.3843e-09\n",
            "\n",
            "Time step 6: Time 35.10 -> 42.12 days\n",
            "  Iteration   1:  Res = 5.4407e-02\n",
            "  Iteration   2:  Res = 1.4731e-04\n",
            "  Iteration   3:  Res = 1.0143e-09\n",
            "\n",
            "Time step 7: Time 42.12 -> 49.13 days\n",
            "  Iteration   1:  Res = 5.0478e-02\n",
            "  Iteration   2:  Res = 1.2742e-04\n",
            "  Iteration   3:  Res = 7.6820e-10\n",
            "\n",
            "Time step 8: Time 49.13 -> 56.15 days\n",
            "  Iteration   1:  Res = 4.6877e-02\n",
            "  Iteration   2:  Res = 1.1048e-04\n",
            "  Iteration   3:  Res = 5.8325e-10\n",
            "\n",
            "Time step 9: Time 56.15 -> 63.17 days\n",
            "  Iteration   1:  Res = 4.3543e-02\n",
            "  Iteration   2:  Res = 9.5794e-05\n",
            "  Iteration   3:  Res = 4.4178e-10\n",
            "\n",
            "Time step 10: Time 63.17 -> 70.19 days\n",
            "  Iteration   1:  Res = 4.0451e-02\n",
            "  Iteration   2:  Res = 8.3042e-05\n",
            "  Iteration   3:  Res = 3.3385e-10\n",
            "\n",
            "Time step 11: Time 70.19 -> 77.21 days\n",
            "  Iteration   1:  Res = 3.7579e-02\n",
            "  Iteration   2:  Res = 7.1967e-05\n",
            "  Iteration   3:  Res = 2.5186e-10\n",
            "\n",
            "Time step 12: Time 77.21 -> 84.23 days\n",
            "  Iteration   1:  Res = 3.4913e-02\n",
            "  Iteration   2:  Res = 6.2353e-05\n",
            "  Iteration   3:  Res = 1.8979e-10\n",
            "\n",
            "Time step 13: Time 84.23 -> 91.25 days\n",
            "  Iteration   1:  Res = 3.2437e-02\n",
            "  Iteration   2:  Res = 5.4012e-05\n",
            "  Iteration   3:  Res = 1.4288e-10\n",
            "\n",
            "Time step 14: Time 91.25 -> 98.27 days\n",
            "  Iteration   1:  Res = 3.0138e-02\n",
            "  Iteration   2:  Res = 4.6777e-05\n",
            "  Iteration   3:  Res = 1.0749e-10\n",
            "\n",
            "Time step 15: Time 98.27 -> 105.29 days\n",
            "  Iteration   1:  Res = 2.8002e-02\n",
            "  Iteration   2:  Res = 4.0505e-05\n",
            "  Iteration   3:  Res = 8.0820e-11\n",
            "\n",
            "Time step 16: Time 105.29 -> 112.31 days\n",
            "  Iteration   1:  Res = 2.6018e-02\n",
            "  Iteration   2:  Res = 3.5068e-05\n",
            "  Iteration   3:  Res = 6.0733e-11\n",
            "\n",
            "Time step 17: Time 112.31 -> 119.33 days\n",
            "  Iteration   1:  Res = 2.4175e-02\n",
            "  Iteration   2:  Res = 3.0356e-05\n",
            "  Iteration   3:  Res = 4.5616e-11\n",
            "\n",
            "Time step 18: Time 119.33 -> 126.35 days\n",
            "  Iteration   1:  Res = 2.2464e-02\n",
            "  Iteration   2:  Res = 2.6273e-05\n",
            "  Iteration   3:  Res = 3.4246e-11\n",
            "\n",
            "Time step 19: Time 126.35 -> 133.37 days\n",
            "  Iteration   1:  Res = 2.0873e-02\n",
            "  Iteration   2:  Res = 2.2737e-05\n",
            "  Iteration   3:  Res = 2.5700e-11\n",
            "\n",
            "Time step 20: Time 133.37 -> 140.38 days\n",
            "  Iteration   1:  Res = 1.9396e-02\n",
            "  Iteration   2:  Res = 1.9674e-05\n",
            "  Iteration   3:  Res = 1.9279e-11\n",
            "\n",
            "Time step 21: Time 140.38 -> 147.40 days\n",
            "  Iteration   1:  Res = 1.8024e-02\n",
            "  Iteration   2:  Res = 1.7022e-05\n",
            "  Iteration   3:  Res = 1.4457e-11\n",
            "\n",
            "Time step 22: Time 147.40 -> 154.42 days\n",
            "  Iteration   1:  Res = 1.6749e-02\n",
            "  Iteration   2:  Res = 1.4726e-05\n",
            "  Iteration   3:  Res = 1.0838e-11\n",
            "\n",
            "Time step 23: Time 154.42 -> 161.44 days\n",
            "  Iteration   1:  Res = 1.5564e-02\n",
            "  Iteration   2:  Res = 1.2738e-05\n",
            "  Iteration   3:  Res = 8.1220e-12\n",
            "\n",
            "Time step 24: Time 161.44 -> 168.46 days\n",
            "  Iteration   1:  Res = 1.4463e-02\n",
            "  Iteration   2:  Res = 1.1018e-05\n",
            "  Iteration   3:  Res = 6.0850e-12\n",
            "\n",
            "Time step 25: Time 168.46 -> 175.48 days\n",
            "  Iteration   1:  Res = 1.3441e-02\n",
            "  Iteration   2:  Res = 9.5289e-06\n",
            "\n",
            "Time step 26: Time 175.48 -> 182.50 days\n",
            "  Iteration   1:  Res = 1.2491e-02\n",
            "  Iteration   2:  Res = 8.2406e-06\n",
            "\n",
            "Time step 27: Time 182.50 -> 189.52 days\n",
            "  Iteration   1:  Res = 1.1608e-02\n",
            "  Iteration   2:  Res = 7.1259e-06\n",
            "\n",
            "Time step 28: Time 189.52 -> 196.54 days\n",
            "  Iteration   1:  Res = 1.0787e-02\n",
            "  Iteration   2:  Res = 6.1616e-06\n",
            "\n",
            "Time step 29: Time 196.54 -> 203.56 days\n",
            "  Iteration   1:  Res = 1.0025e-02\n",
            "  Iteration   2:  Res = 5.3275e-06\n",
            "\n",
            "Time step 30: Time 203.56 -> 210.58 days\n",
            "  Iteration   1:  Res = 9.3166e-03\n",
            "  Iteration   2:  Res = 4.6060e-06\n",
            "\n",
            "Time step 31: Time 210.58 -> 217.60 days\n",
            "  Iteration   1:  Res = 8.6584e-03\n",
            "  Iteration   2:  Res = 3.9819e-06\n",
            "\n",
            "Time step 32: Time 217.60 -> 224.62 days\n",
            "  Iteration   1:  Res = 8.0467e-03\n",
            "  Iteration   2:  Res = 3.4423e-06\n",
            "\n",
            "Time step 33: Time 224.62 -> 231.63 days\n",
            "  Iteration   1:  Res = 7.4783e-03\n",
            "  Iteration   2:  Res = 2.9756e-06\n",
            "\n",
            "Time step 34: Time 231.63 -> 238.65 days\n",
            "  Iteration   1:  Res = 6.9501e-03\n",
            "  Iteration   2:  Res = 2.5721e-06\n",
            "\n",
            "Time step 35: Time 238.65 -> 245.67 days\n",
            "  Iteration   1:  Res = 6.4593e-03\n",
            "  Iteration   2:  Res = 2.2232e-06\n",
            "\n",
            "Time step 36: Time 245.67 -> 252.69 days\n",
            "  Iteration   1:  Res = 6.0031e-03\n",
            "  Iteration   2:  Res = 1.9216e-06\n",
            "\n",
            "Time step 37: Time 252.69 -> 259.71 days\n",
            "  Iteration   1:  Res = 5.5792e-03\n",
            "  Iteration   2:  Res = 1.6608e-06\n",
            "\n",
            "Time step 38: Time 259.71 -> 266.73 days\n",
            "  Iteration   1:  Res = 5.1852e-03\n",
            "  Iteration   2:  Res = 1.4354e-06\n",
            "\n",
            "Time step 39: Time 266.73 -> 273.75 days\n",
            "  Iteration   1:  Res = 4.8191e-03\n",
            "  Iteration   2:  Res = 1.2405e-06\n",
            "\n",
            "Time step 40: Time 273.75 -> 280.77 days\n",
            "  Iteration   1:  Res = 4.4789e-03\n",
            "  Iteration   2:  Res = 1.0721e-06\n",
            "\n",
            "Time step 41: Time 280.77 -> 287.79 days\n",
            "  Iteration   1:  Res = 4.1627e-03\n",
            "  Iteration   2:  Res = 9.2645e-07\n",
            "\n",
            "Time step 42: Time 287.79 -> 294.81 days\n",
            "  Iteration   1:  Res = 3.8688e-03\n",
            "  Iteration   2:  Res = 8.0061e-07\n",
            "\n",
            "Time step 43: Time 294.81 -> 301.83 days\n",
            "  Iteration   1:  Res = 3.5957e-03\n",
            "  Iteration   2:  Res = 6.9184e-07\n",
            "\n",
            "Time step 44: Time 301.83 -> 308.85 days\n",
            "  Iteration   1:  Res = 3.3419e-03\n",
            "  Iteration   2:  Res = 5.9784e-07\n",
            "\n",
            "Time step 45: Time 308.85 -> 315.87 days\n",
            "  Iteration   1:  Res = 3.1060e-03\n",
            "  Iteration   2:  Res = 5.1660e-07\n",
            "\n",
            "Time step 46: Time 315.87 -> 322.88 days\n",
            "  Iteration   1:  Res = 2.8867e-03\n",
            "  Iteration   2:  Res = 4.4639e-07\n",
            "\n",
            "Time step 47: Time 322.88 -> 329.90 days\n",
            "  Iteration   1:  Res = 2.6830e-03\n",
            "  Iteration   2:  Res = 3.8571e-07\n",
            "\n",
            "Time step 48: Time 329.90 -> 336.92 days\n",
            "  Iteration   1:  Res = 2.4936e-03\n",
            "  Iteration   2:  Res = 3.3328e-07\n",
            "\n",
            "Time step 49: Time 336.92 -> 343.94 days\n",
            "  Iteration   1:  Res = 2.3176e-03\n",
            "  Iteration   2:  Res = 2.8797e-07\n",
            "\n",
            "Time step 50: Time 343.94 -> 350.96 days\n",
            "  Iteration   1:  Res = 2.1540e-03\n",
            "  Iteration   2:  Res = 2.4882e-07\n",
            "\n",
            "Time step 51: Time 350.96 -> 357.98 days\n",
            "  Iteration   1:  Res = 2.0020e-03\n",
            "  Iteration   2:  Res = 2.1498e-07\n",
            "\n",
            "Time step 52: Time 357.98 -> 365.00 days\n",
            "  Iteration   1:  Res = 1.8607e-03\n",
            "  Iteration   2:  Res = 1.8575e-07\n",
            "ans = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "4_1aPl_EGXYV",
        "outputId": "4f3bf557-9893-41f2-ec2a-18c887bcdf84"
      },
      "source": [
        "# Specify dimension\n",
        "dimension = (nx,ny,nz)\n",
        "\n",
        "# Input for plots\n",
        "plane, position = \"xy\", 2\n",
        "\n",
        "numSteps, totTime = 52, 365 # day\n",
        "steps = [2, 5, 10, 20]\n",
        "\n",
        "# Plot pressure for every timestep\n",
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "for i in range(len(steps)):\n",
        "  directory = \"/content/result_oil_1phase/\"\n",
        "  filename = \"pressure{}.mat\".format(steps[i])\n",
        "\n",
        "  # Get cell data \n",
        "  cube = pymrst.getCellData(directory, filename, dimension=(nx,ny,nz))\n",
        "\n",
        "  # Convert to barsa\n",
        "  cube = cube/barsa()\n",
        "\n",
        "  # Plot for every timestep\n",
        "  day = totTime / numSteps * steps[i]\n",
        "  plt.subplot(2,2,i+1)\n",
        "  pymrst.plotCellData(cube, plane, position, cmap=\"plasma\")\n",
        "  plt.title(\"Pressure on {} Plane at Slice {} (Day {:.1f})\".format(plane, position, day),\n",
        "            size=15, pad=15)  \n",
        "\n",
        "plt.tight_layout(1.7)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ZILdm_WLE-vq",
        "outputId": "e3b5467a-bb9a-4c5e-ee00-c8ba3a7f59ef"
      },
      "source": [
        "from pymrst_units import *\n",
        "\n",
        "# Plot well solutions\n",
        "directory = \"/content/result_oil_1phase/\"\n",
        "\n",
        "time = pymrst.getWellSol(directory, \"time.mat\") / day() \n",
        "qo = pymrst.getWellSol(directory, \"qo.mat\") / (stb()/day())\n",
        "Pwf = pymrst.getWellSol(directory, \"Pwf.mat\") / barsa() # convert to barsa\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(time, Pwf, \".-\", color=\"purple\")\n",
        "plt.grid()\n",
        "plt.title(\"Pwf\")\n",
        "plt.xlabel(\"Day\"); plt.ylabel(\"Pwf [bar]\")\n",
        "plt.xlim(0,365)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.step(time, qo, color=\"green\")\n",
        "plt.grid()\n",
        "plt.title(\"Oil rate\")\n",
        "plt.xlabel(\"Day\"); plt.ylabel(\"Qo [STB/D]\")\n",
        "plt.xlim(0,365)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}